Set portfolio.py START to today so the $1k book tracks these wallets from
now forward (clean forward test, no in-sample backfill). Equity resets to
~$1,000; the daily run fills it in as bets resolve.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Update the dev-facing docs so others can follow the current system:
- live/README: copy-positive-holder selection (replaces lead-time gate),
Copy P&L as the copyability metric, new Paper portfolio (portfolio.py)
+ Dashboard feeds (watch_sharps.json / portfolio.json) sections, the
full 8-step daily flow, the cache rolling-180d/replace retention gotcha,
and a Copy execution (copybot/sync_floors, separate WIP) note.
- README: top portfolio is now precomputed off the cache (correct
recycling), judge by Copy P&L not win%, copy execution is separate.
- FINDINGS: capital-recycling / $1k-book section + repo-layout refresh.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New portfolio.py replays the followed wallets' conviction bets ($1k/$50,
hold-to-resolution) from cache.duckdb — which has res_t, so capital
RECYCLES at the true resolution time (no client-side clob storms, no
phantom-locked capital) and covers the full bet history (~40 pages, not
the browser's 4). Writes portfolio.json (equity, splits, current/
resolved/missed tables, per-wallet); wired into daily.sh + publish. The
dashboard now renders the top page from this feed (1 request) with the
client-side replay kept as a fallback.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the blunt 24h lead gate with a copy-replay selection run on EVERY
conviction wallet: keep only those that are copy-positive AND have a
genuine hold-to-resolution edge (held_pnl>0, held win-rate>=55% over >=8
resolved held bets), active in 30d, with a light >=1h lead floor vs true
snipers. display_stats now splits copy P&L into scalp vs held legs and
uses fresh-positions resolution (clob fallback) so it's cheap enough to
run on all ~229 wallets.
Effect: drops scalper-traps whose win% looked great but lose copied
(ArbTrader, iohihoo, S888, ttj01) and the old lead gate's discards;
surfaces fast-resolving copy-positive holders it used to throw away
(raid3r, 0x6d1A94f4, 0xec1d07e5, mantaray, earthisgood, shisan...).
14 copy-positive holders now in watch_sharps.json.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The daily run regenerates dashboard.html but the publish step never
committed it, leaving it perpetually modified. Add it to the publish git
add list and commit the current snapshot.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
README/FINDINGS/live/README updated for today's discovery: the sharps
win% is a position snapshot that over-counts scalpers; copy_pnl (flat-$50
replay, authoritative clob resolution) is the real copyability signal.
Most "sharps" lose copied; only Kruto2027 + fortuneking are copy-positive
(now the tracked pair). Refreshed stale counts (50 -> ~31 after the
30d-active filter) and noted position-level conviction.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
display_stats now computes copy_pnl: what a flat-$50 copier of a wallet's
conviction bets ACTUALLY realizes since Jun 1 — replays their entries,
mirrors their exits, and settles held bets at AUTHORITATIVE clob
resolution (winner by token_id). This is the truth for copyability and
exposes scalpers whose position win% looks great but lose when copied
(ArbTrader 99.5% conv win but -$793 copy; iohihoo 88.7% but -$749).
Position win%/record/P&L stay on the cache (large 180d sample). Added a
clob resolver (_clob_winner) + activity-replay; only Kruto2027 (+1184),
S888, oliman2, fortuneking (+430) etc. are positive to copy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
validate_timing now filters the sharp list to wallets that have traded in
the last 30 days (last_trade from the /activity pull), so watch_sharps.json
only lists currently-active sharps — not just hidden client-side. Dropped
10 of 41 on this run (-> 31 active). The dashboard keeps its own 30d filter
as a safety net for daily-stale feeds.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
display_stats now emits conv30_win/won/lost/pnl — conviction (top-20%)
bets resolved in the last 30 days — for a new "30 Day Conv Record" column
on the dashboard alongside the lifetime record.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
daily.sh now POSTs a summary (finish time, sharp count, push status) to a
Discord webhook after the publish step. The webhook URL is read from the
gitignored config.json (daily_webhook key) so it never lands in the public
repo; the ping is skipped if the key is absent. Sends a browser User-Agent
(Discord 403s requests without one).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
display_stats now emits avg_bet = average stake of the wallet's
conviction (top-20%) bets, for a new dashboard column after the P&L
columns.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
validate_timing.py now writes name, conv_win, conv_won/lost, conv_pnl,
realized_pnl, last_conv_bet and last_trade per sharp, so the dashboard
reads everything from the feed in ONE request instead of 3 data-api
calls per wallet (no more rate-limit storms). Stats come from the cache
(survivorship-correct): conviction win%/record/P&L over ALL of the
wallet's top-20%-stake bets; realized P&L over the last 500 resolved
bets. Resolved P&L per bet = stake*(1-p)/p if won else -stake.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recent_markets() only stopped on end-of-data (page <100) or MAX_SCAN.
When gamma 422s past its max offset, every page in a wave returns "ERR"
and is skipped — so `scanned` never grows and `ended` never trips,
spinning forever. This wedged every daily.sh run at "scanned 2,100…"
(step 1/6), so the cron never actually completed. Now bail after a
second consecutive all-error wave (one is tolerated; _page already
retries transient errors).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add the sharps pipeline (conviction_scan.py + validate_timing.py) to the
daily run so watch_sharps.json self-updates, and a publish step that
commits + pushes the refreshed outputs (watch_skilled / watch_sharps /
conviction_wallets) so the live dashboard at jaxperro.com/trading picks
up the new set. Also force-refresh the sharps watchlist (not just
skilled) before the cache top-up for accurate forward stats. Publish
does pull --rebase before push so a diverged remote can't wedge it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The lead-time gate in validate_timing.py is a COPYABILITY heuristic, not
proof of inside information: a short entry->resolution lead can be a
genuine insider OR just someone who trades fast-resolving markets (live
sports, hourly) well — indistinguishable, and irrelevant for copy
purposes since either way the window is too tight to mirror. Rename the
verdict accordingly. Gate unchanged (median lead >=24h to count as a
copyable sharp; 50 sharps). Docs updated to current p80 numbers
(218 profile matches, 62/83 forward-profitable, 50 sharps).
The genuine insider-detection scanner (insider.py, z-score / Bubblemaps
fingerprint) keeps its name — that's a different, correctly-named thing.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Swap the flat $200 conviction cutoff for a per-wallet percentile (top
20% of each wallet's own stake sizes) everywhere it was used:
- cache.py: canonical CONV_PCTILE=0.80 + conv_cutoff() helper (matches
the dashboard's pctl: filter >0, sort, linear interp)
- conviction_scan.py: per-wallet quantile_cont(size,0.8) in SQL, was
`size >= 200`
- validate_timing.py, pnl_focused.py: use cache.conv_cutoff
Rationale + validation: p80 reproduces flat-$200's win-rate lift on the
sharps while adapting to scale (a whale's $200 isn't conviction, a
minnow's is). Re-running the pipeline under p80: scan finds 218 profile
wallets (was 69), forward 62/83 profitable (p~0), +16% pooled ROI — edge
persists out-of-sample. Regenerated conviction_wallets.json /
watch_sharps.json; docs updated. skill.py/strategy.py/insider.py
untouched (score over all bets / size as copyability heuristic only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
README/FINDINGS/live-README now cover the repeatable find: score high-conviction
(>=$200) bets, validate forward (25/37 profitable, p=0.024), then a lead-time
gate drops uncopyable insiders -> 23 validated copyable sharps shown live on
jaxperro.com/trading.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
conviction_scan.py finds wallets whose high-conviction (>=$200) bets win on
uncertain (~0.4-0.6) markets — real edge, not favorite-riding — trained pre-June
and validated June (25/37 stayed profitable forward, p=0.024). validate_timing.py
applies the entry->resolution lead-time gate that separates copyable sharps from
uncopyable insiders: of 69 matches, 21 were insiders (lead <6h), leaving 23
validated copyable sharps (watch_sharps.json) now shown on jaxperro.com/trading.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
live/: operationalizes the LBS/Yale "skilled ~3%" result against the live
data-api. Enumerate recent liquid markets -> top traders -> candidate pool;
cache every wallet's resolved bets once in DuckDB (~26k wallets / 12.5M bets,
keyed by per-bet resolution time so any cutoff re-scores in seconds); 5-gate
skill funnel (n>=15, z>0, BH-FDR, split-half OOS, MM/bot cap); dashboard +
daily refresh.
Key finding: copying the high-win-rate "favorite-rider" cohort looks +23.6%
in-sample but loses -7.4% once selected on pre-June-1 data only (99% -> 68%
win rate) — selection bias, reproducing the paper's "lucky winners revert"
result on live data. Win rate != edge, again.
wide/: bulk subgraph->DuckDB scanner (survivorship-bias-free over all wallets),
but the public subgraph is frozen at Jan 2026 -> historical tool only.
Large local data (*.duckdb, candidates.json, *_scored.json, history/) gitignored.
README + FINDINGS updated with the current logic and the clean result.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Brings the README current with the deployed pieces: the push-based Discord
watcher and the live $1,000 client-side paper portfolio at jaxperro.com/trading
(enter-on-entry, hold-to-resolution, Liquid/Invested/Realized/Missed). Updates
the pipeline diagram and tools table; notes the capital-constraint finding the
tracker surfaced.
Drops the market-maker bots (paspor, donthackme), the longshot lottery
(qqqq88888), and the un-followable in-game trader (Xixihaha008). Concentrates
on high-z, followable, non-bot wallets.
Pure-stdlib repo had no language signal, so Nixpacks failed to build an image.
requirements.txt (empty) triggers Python detection; nixpacks.toml pins python311
and sets the start command to the webhook receiver.
- Add copyback.py (in-sample copy backtest), oos.py (out-of-sample test),
huntwide.py (wide insider sweep) — completes the detect→hunt→validate→watch
pipeline.
- Rewrite README around Winning Wallet Finder: the z-score idea explained, how
the pieces fit, quickstart, data sources, live-watcher setup, honest verdict.
- Extend FINDINGS with the insider-detection results and the in-sample vs
out-of-sample copy verdict (+545% in-sample collapsed to one-wallet variance
out-of-sample).
- Refresh config.example.json to the current schema (discord/alchemy/watch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
webhook_receiver.py: zero-dep stdlib HTTP server that receives Alchemy
Address-Activity webhook POSTs, confirms/enriches the trade via Polymarket
data-API, and pushes a Discord alert — fires only on a real trade, no polling.
Cloud-ready: secrets via env (DISCORD_WEBHOOK, ALCHEMY_SIGNING_KEY), non-secret
watch list in committed watch.json, binds to $PORT, /health endpoint,
optional HMAC signature verification, in-memory dedup. Procfile for one-command
deploy. config.json (secrets) stays gitignored.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Sweeps a list of insider-prone markets, scores each market's top traders with
insider.analyze, and runs funding-cluster ring detection. First real hunt
flagged DREAMBIG. (z=8.9, p~2e-19) and qcp14 (z=5.3, p~4e-8) on the Iran
ceasefire-extension market — insider-grade improbability — with no operator
rings (all independently/exchange-funded; exchange-funded wallets aren't
linkable, a known limit).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Moved the 8 tested-and-failed strategy tools into archive/ (copytrade, backtest,
edge_research, lookback, table_77, lp_screener, lp_paper, xarb) with an
archive/README explaining each. Root now holds the keepers: insider.py (made
self-sufficient — dropped the copytrade load_json dependency) and smart_money.py
(data foundation). New FINDINGS.md is the honest scorecard: six systematic
public-data edges all efficient/illusory, the win-rate survivorship-bias
finding, and the one real signal (z-score improbability + funding clustering).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pulls each wallet's USDC funding history (alchemy_getAssetTransfers, full
history, no 10k-block cap) and links wallets sharing a funder = likely same
operator. Critical refinement: a shared funder only counts if its OWN outbound
degree is small (personal hub <=15 recipients); exchanges/bridges fan out to
hundreds and must be excluded or everything false-links. Verified: the 10
watchlist wallets shared 11 infra funders (Coinbase-style) and looked like one
ring until the degree filter correctly cleared them to independent.
Runs automatically after --market/--scan when alchemy_key is in config.json
(gitignored). README updated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replicates the Bubblemaps / 60 Minutes per-wallet insider methodology on the
public data API:
- Improbability z-score / p-value: wins vs the wins entry odds imply (beating
the market's own pricing) — the rigorous edge metric the project was after,
unlike biased win-rate or variance-driven PnL
- Pre-resolution timing, fresh-wallet (/traded count), sizing signals
- Scoring GATED by improbability so losing sports bettors (entering <24h before
a game is normal) no longer false-flag
- Modes: --scan leaderboard, --market <conditionId|slug> (score a market's
traders, the Bubblemaps approach), --wallet deep profile
Findings: leaderboard has no extreme insiders (max z~2.3, high-vol sharps);
scanning a market's traders surfaces real edges (e.g. arimnestos z=4.0 p~3e-5
over 2205 bets). Funding-cluster linking needs a Polygonscan/Alchemy key
(public RPC getLogs capped at 10k blocks). README documents methodology +
the project-wide conclusion.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
xarb.py pulls Polymarket (Gamma) + Kalshi (elections API, ~65k markets),
matches the same contract (token overlap + same resolution month + exact
numeric match on thresholds/scores/dates), and computes both arb directions
with Kalshi's 0.07*P*(1-P) taker fee.
Verified verdict: no retail cross-venue arb. On liquid, identical, cleanly-
matched contracts the venues agree to ~1c and locking both sides costs >$1
after fees (worked example: Brazil-Morocco BTTS, PM 0.46/0.47 vs Kalshi
0.47/0.48 -> every direction negative). The big apparent edges are false
matches, illiquid wide-spread markets, or stale snapshot timing.
README now records the full project conclusion: six systematic public-data
edges tested, all efficient/illusory. Durable edge needs speed/infra, private
information, or liquidity provision -- not a turnkey public-data scanner.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fixes that removed optimistic bias and fragility before extended runs:
- enforce min_size: only accrue rewards (and only screen markets) where our
per-side size actually qualifies — at $1k split many ways, the fattest
pools are unreachable, which the screen now reflects honestly
- handle Polymarket's Q_min: skip markets priced outside 0.10-0.90, and score
single-sided quoting at 1/3 share
- price-aware inventory cap + capped fills so one fill at a low price can't
overshoot the intended position and distort the bleed
- cap dt per poll so a stall/sleep can't over-credit rewards
- wrap the periodic re-screen in try/except so a network blip can't kill an
overnight run
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The loop screened once and quoted the same markets forever; short-lived
prop markets would resolve and silently stop earning. Now it re-screens every
--refresh seconds, drops markets that fell out of the fresh set (banking their
rewards+inventory into a retired accumulator), and adds fresh ones — so
cumulative net survives rotation over multi-day runs.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Simulates two-sided quoting on the screener's top low-vol markets against the
live order book, tracking net = rewards accrued - adverse-selection bleed.
Clean cash + mark-to-market accounting; fills modeled when midpoint crosses a
resting quote (slightly pessimistic on fill rate); rewards accrue by
score-share of each pool. Discord summaries + state persistence so it can run
for days. This is the decisive, no-money test before any funded/hosted bot.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Ranks Polymarket's ~8000 reward-eligible markets by risk-adjusted LP yield:
reward pool / order-book competition near mid (gross APR for a $1000
two-sided position), penalized by 24h midpoint volatility (adverse-selection
proxy) and time-to-resolution. One-shot snapshot -> lp_markets.csv. README
documents the rewards mechanics, the screener, and the open caveats before
the paper LP loop. Generated data files gitignored.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Adds edge_research.py (scan ~2000 wallets for reliable/copyable weekly edge),
lookback.py (long-window half-split out-of-sample read), and table_77.py
(aggregate a wallet set to CSV). README now leads with a research log
capturing the key findings: win-rate survivorship bias, win-rate != EV,
flat-size copying is -EV, the reliable edge is rare and skews to young
accounts, and ROI is inversely related to bet size. Generated data files are
gitignored.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The scanner measured win rate over /closed-positions only, but Polymarket
only redeems winning shares — losers sit unredeemed in /positions at
curPrice 0 and never enter closed-positions. That made win rates wildly
inflated (e.g. 90.6% vs a true 48.3%). Win rate now unions both endpoints
over a 90-day window. With the honest metric, ~no top wallet exceeds ~60%;
true rates cluster near 50%.
Also:
- backtest.py: replay a watchlist over a recent window, fill at historical
price, mark outcomes from resolution. A 7d run of 4 top wallets returned
-48%, confirming flat-size entry-copying is -EV at ~50% hit rates.
- copytrade.py: add max_position_usd cap (proportional adds could otherwise
balloon one position to the whole exposure limit) and Discord webhook
alerts on every would-be trade.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Watches a wallet list and copies trades onto your account: % -of-bankroll
sizing, proportional entry/exit mirroring, 5% price guard, and a no-backfill
rule for positions held before start. Paper mode by default; live trading is
gated behind config + --live + a typed confirmation, with hard risk caps
(per-trade, daily, total exposure, open positions, price bounds). Live
execution via py-clob-client (lazy import). Credentials/state gitignored.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Dashboard + terminal tool that pulls the 7d/30d/all leaderboards, measures
each wallet's win rate over its most recent resolved bets, and its distinct
markets traded per week. Zero dependencies (Python 3 stdlib only).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
jaxperro